1. ICML Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation (@sla) Preferred Infrastructure

2. ICML2013 l l T-PRIMAL20-30 l PFI l l NIPS l PFI 2

3. l ICML2013 l l RGB-D Agenda

4. ICML2013: 4

5. ICML2013: 5

6. ICML2013: 1. Machine Learning at Scale with GraphLab by Carlos Guestrin l GAS(Gather-Apply-Scatter) l GraphLab2GraphLab3 2. High-dimensional Sampling Algorithms and their Applications by Santosh Vempala l Convex, Convex, and Convex 3. Acoustic Modeling and Deep Learning for Speech Recognition by Vincent Vanhoucke (Google Voice Search) l Deep Learning l Deep Belief Networks [Bengio+, 2007] l GPGPU l Data(+dropout) l 1011HintonGoogle 6

7. ICML2013Sparse, Deep, and Random 7 Sparse, Random, MultiBandit KernelSVMReinforcementBayesian http://www.machinedlearnings.com/2013/06/icml-2013-sparse-deep-and-random.html

8. l @sla : "Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity... l @beam2d: "Local Deep Kernel Learning for Efficient Non-linear SVM Prediction l @conditional: "Vanishing Component Analysis l @jkomiyama_ : "Active Learning for Multi-Objective Optimization l @kisa12012 : "Large-Scale Learning with Less RAM via Randomization l @Quasi_quant2010 : "Topic Discovery through Data Dependent and Random Projections l @tabe2314 : "Fast Image Tagging l @unnonouno : "ELLA: An Efficient Lifelong Learning Algorithm l @sleepy_yoshi : "Distributed Training of Large-scale Logistic Models" 8 ---Sparse ---Deep (?) ---Random ---Others(Spatio-Temporal, Component Analysis, Multi-taskDistributed)

9. l ICML2013 l l RGB-D Agenda

10. i.i.d l l l i.i.d l i.i.d l 10

11. : l 3 l Robot Learning l Machine Learning with Test-Time Budgets l Learning with Sequential Models l l Cost-sensitive Learning l Imitation Learning / Interactive Learning l Reinforcement Learning l Imperative Learning (Data Search/Aggregation) l l Anytime l l l Feature11

12. : DAgger [Ross+, AISTATS11] l Dataset Aggregator 12 Ross et al., A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, AISTATS'11

13. l ICML2013 l l RGB-D Agenda

14. Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation" l 1: Hemi Koppula (Cornell University) l 2: Ashtosh Saxena (Cornell University) l Andrew NgRobot/CV l Robot LearningInvited Talk l RGB-D l l Activity Detection: l Sub-activity l Activity Anticipation: 14

15. l http://pr.cs.cornell.edu/anticipation/ 15

16. l Cutting-plane training of structural SVMs [Joachims+, MLJ2009] 16